| Página principal > Artículos > An image-to-image adversarial network to generate high resolution wind data over complex terrains from weather predictions > MARC |
000147988 001__ 147988 000147988 005__ 20250110163828.0 000147988 0247_ $$2doi$$a10.1016/j.engappai.2024.109533 000147988 0248_ $$2sideral$$a141585 000147988 037__ $$aART-2025-141585 000147988 041__ $$aeng 000147988 100__ $$aMilla-Val, Jaime 000147988 245__ $$aAn image-to-image adversarial network to generate high resolution wind data over complex terrains from weather predictions 000147988 260__ $$c2025 000147988 5060_ $$aAccess copy available to the general public$$fUnrestricted 000147988 5203_ $$an this work, we propose a Machine Learning method to predict detailed wind fields over extensive, complex terrains. The ability to predict local wind fields is becoming increasingly important for a range of applications, including sports in Nature, large outdoors events, light-aircraft flying, or the management of natural disasters. The intricate nature of wind dynamics, particularly in regions with complex orography such as a mountain range, presents a major challenge to traditional forecasting models. This work presents an efficient way to predict local wind conditions with a high resolution, similar to that of Computational Fluid Dynamics (CFD), in large geographical areas with complex terrain, using the results from relatively coarse (and therefore economical) data from Numerical Weather Prediction (NWP). To achieve this goal, we developed a conditional Generative Adversarial Neural network model (cGAN) to convert NWP data into CFD-like simulations. We apply the method to a rugged region in the Pyrenees mountain range in Spain. The results show that the proposed model outperforms traditional Machine Learning methods, such as Support Vector Machines (SVM), in terms of accuracy and computational efficiency. The method is four orders of magnitude faster than traditional CFD. Mean Average Errors of 1.36 m/s for wind speed and 18.73°for wind direction are obtained with the proposed approach. 000147988 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T32-23R$$9info:eu-repo/grantAgreement/ES/MICIN/DIN2019-010452$$9info:eu-repo/grantAgreement/ES/MICINN/TED2021-131861B-I00 000147988 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000147988 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000147988 700__ $$aMontañés, Carlos 000147988 700__ $$0(orcid)0000-0001-6205-5160$$aFueyo, Norberto$$uUniversidad de Zaragoza 000147988 7102_ $$15001$$2600$$aUniversidad de Zaragoza$$bDpto. Ciencia Tecnol.Mater.Fl.$$cÁrea Mecánica de Fluidos 000147988 773__ $$g139, B (2025), 109533 [14 pp.]$$pEng. appl. artif. intell.$$tENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE$$x0952-1976 000147988 8564_ $$s6323440$$uhttps://zaguan.unizar.es/record/147988/files/texto_completo.pdf$$yVersión publicada 000147988 8564_ $$s2722586$$uhttps://zaguan.unizar.es/record/147988/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000147988 909CO $$ooai:zaguan.unizar.es:147988$$particulos$$pdriver 000147988 951__ $$a2025-01-10-14:24:36 000147988 980__ $$aARTICLE
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